

'''simple docstring'''





import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			List[str]:
					lowerCamelCase_  				=      torch.exp(__UpperCamelCase	)
					lowerCamelCase_  				=      torch.sum(__UpperCamelCase    ,dim=1	)  # sum of exp(x_i)
					lowerCamelCase_  				=      torch.sum(x * exp_x    ,dim=1	)  # sum of x_i * exp(x_i)
					return torch.log(__UpperCamelCase	) - B / A


class  UpperCAmelCase (							nn.Module    ):

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     Union[str, Any]:






							'''simple docstring'''



							super().__init__()
							lowerCamelCase_  				=      config.output_attentions
							lowerCamelCase_  				=      config.output_hidden_states
							lowerCamelCase_  				=      nn.ModuleList([BertLayer(lowerCAmelCase__		) for _ in range(config.num_hidden_layers		)]		)
							lowerCamelCase_  				=      nn.ModuleList([BertHighway(lowerCAmelCase__		) for _ in range(config.num_hidden_layers		)]		)

							lowerCamelCase_  				=      [-1 for _ in range(config.num_hidden_layers		)]
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     List[str]:






							'''simple docstring'''



							if (type(lowerCAmelCase__		) is float) or (type(lowerCAmelCase__		) is int):
												for i in range(len(self.early_exit_entropy		)		):
																	lowerCamelCase_  				=      x
							else:
												lowerCamelCase_  				=      x
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      pooler.state_dict()
							for highway in self.highway:
												for name, param in highway.pooler.state_dict().items():
																	param.copy_(loaded_model[name]		)






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      ()
							lowerCamelCase_  				=      ()
							lowerCamelCase_  				=      ()
							for i, layer_module in enumerate(self.layer		):
												if self.output_hidden_states:
																	lowerCamelCase_  				=      all_hidden_states + (hidden_states,)

												lowerCamelCase_  				=      layer_module(
												    lowerCAmelCase__   ,			lowerCAmelCase__   ,			head_mask[i]   ,			lowerCAmelCase__   ,			lowerCAmelCase__		)
												lowerCamelCase_  				=      layer_outputs[0]

												if self.output_attentions:
																	lowerCamelCase_  				=      all_attentions + (layer_outputs[1],)

												lowerCamelCase_  				=      (hidden_states,)
												if self.output_hidden_states:
																	lowerCamelCase_  				=      current_outputs + (all_hidden_states,)
												if self.output_attentions:
																	lowerCamelCase_  				=      current_outputs + (all_attentions,)

												lowerCamelCase_  				=      self.highway[i](lowerCAmelCase__		)
												# logits, pooled_output

												if not self.training:
																	lowerCamelCase_  				=      highway_exit[0]
																	lowerCamelCase_  				=      entropy(lowerCAmelCase__		)
																	lowerCamelCase_  				=      highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
																	lowerCamelCase_  				=      all_highway_exits + (highway_exit,)

																	if highway_entropy < self.early_exit_entropy[i]:
																						lowerCamelCase_  				=      (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																						raise HighwayException(lowerCAmelCase__   ,			i + 1		)
												else:
																	lowerCamelCase_  				=      all_highway_exits + (highway_exit,)

        # Add last layer
							if self.output_hidden_states:
												lowerCamelCase_  				=      all_hidden_states + (hidden_states,)

							lowerCamelCase_  				=      (hidden_states,)
							if self.output_hidden_states:
												lowerCamelCase_  				=      outputs + (all_hidden_states,)
							if self.output_attentions:
												lowerCamelCase_  				=      outputs + (all_attentions,)

							lowerCamelCase_  				=      outputs + (all_highway_exits,)
							return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits


@add_start_docstrings(
    'The Bert Model transformer with early exiting (DeeBERT). '  , _UpperCAmelCase  , )
class  UpperCAmelCase (							_UpperCAmelCase    ):

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     Tuple:






							'''simple docstring'''



							super().__init__(lowerCAmelCase__		)
							lowerCamelCase_  				=      config

							lowerCamelCase_  				=      BertEmbeddings(lowerCAmelCase__		)
							lowerCamelCase_  				=      DeeBertEncoder(lowerCAmelCase__		)
							lowerCamelCase_  				=      BertPooler(lowerCAmelCase__		)

							self.init_weights()
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							self.encoder.init_highway_pooler(self.pooler		)
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							return self.embeddings.word_embeddings
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      value
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



							for layer, heads in heads_to_prune.items():
												self.encoder.layer[layer].attention.prune_heads(lowerCAmelCase__		)






		@add_start_docstrings_to_model_forward(lowerCAmelCase__		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			)  ->     str:






							'''simple docstring'''



							if input_ids is not None and inputs_embeds is not None:
												raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'		)
							elif input_ids is not None:
												lowerCamelCase_  				=      input_ids.size()
							elif inputs_embeds is not None:
												lowerCamelCase_  				=      inputs_embeds.size()[:-1]
							else:
												raise ValueError('You have to specify either input_ids or inputs_embeds'		)

							lowerCamelCase_  				=      input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
												lowerCamelCase_  				=      torch.ones(lowerCAmelCase__   ,			device=lowerCAmelCase__		)
							if encoder_attention_mask is None:
												lowerCamelCase_  				=      torch.ones(lowerCAmelCase__   ,			device=lowerCAmelCase__		)
							if token_type_ids is None:
												lowerCamelCase_  				=      torch.zeros(lowerCAmelCase__   ,			dtype=torch.long   ,			device=lowerCAmelCase__		)

							# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
							# ourselves in which case we just need to make it broadcastable to all heads.
							lowerCamelCase_  				=      self.get_extended_attention_mask(lowerCAmelCase__   ,			lowerCAmelCase__   ,			lowerCAmelCase__		)

							# If a 2D ou 3D attention mask is provided for the cross-attention
							# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
							if encoder_attention_mask.dim() == 3:
												lowerCamelCase_  				=      encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
												lowerCamelCase_  				=      encoder_attention_mask[:, None, None, :]

							lowerCamelCase_  				=      encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
							lowerCamelCase_  				=      (1.0 - encoder_extended_attention_mask) * -10000.0

							# Prepare head mask if needed
							# 1.0 in head_mask indicate we keep the head
							# attention_probs has shape bsz x n_heads x N x N
							# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
							# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
							lowerCamelCase_  				=      self.get_head_mask(lowerCAmelCase__   ,			self.config.num_hidden_layers		)

							lowerCamelCase_  				=      self.embeddings(
							    input_ids=lowerCAmelCase__   ,			position_ids=lowerCAmelCase__   ,			token_type_ids=lowerCAmelCase__   ,			inputs_embeds=lowerCAmelCase__		)
							lowerCamelCase_  				=      self.encoder(
							    lowerCAmelCase__   ,			attention_mask=lowerCAmelCase__   ,			head_mask=lowerCAmelCase__   ,			encoder_hidden_states=lowerCAmelCase__   ,			encoder_attention_mask=lowerCAmelCase__   ,			)
							lowerCamelCase_  				=      encoder_outputs[0]
							lowerCamelCase_  				=      self.pooler(lowerCAmelCase__		)

							lowerCamelCase_  				=      (
							    sequence_output,
							    pooled_output,
							) + encoder_outputs[
							    1:
							]  # add hidden_states and attentions if they are here
							return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits


class  UpperCAmelCase (							_UpperCAmelCase    ):

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      message
							lowerCamelCase_  				=      exit_layer  # start from 1!


class  UpperCAmelCase (							nn.Module    ):

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[int]:






							'''simple docstring'''



							super().__init__()
							lowerCamelCase_  				=      BertPooler(lowerCAmelCase__		)
							lowerCamelCase_  				=      nn.Dropout(config.hidden_dropout_prob		)
							lowerCamelCase_  				=      nn.Linear(config.hidden_size   ,			config.num_labels		)






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      encoder_outputs[0]
							lowerCamelCase_  				=      self.pooler(lowerCAmelCase__		)
							# "return" pooler_output

							# BertModel
							lowerCamelCase_  				=      (pooler_input, pooler_output) + encoder_outputs[1:]
							# "return" bmodel_output

							# Dropout and classification
							lowerCamelCase_  				=      bmodel_output[1]

							lowerCamelCase_  				=      self.dropout(lowerCAmelCase__		)
							lowerCamelCase_  				=      self.classifier(lowerCAmelCase__		)

							return logits, pooled_output


@add_start_docstrings(
    'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '  , _UpperCAmelCase  , )
class  UpperCAmelCase (							_UpperCAmelCase    ):

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[int]:






							'''simple docstring'''



							super().__init__(lowerCAmelCase__		)
							lowerCamelCase_  				=      config.num_labels
							lowerCamelCase_  				=      config.num_hidden_layers

							lowerCamelCase_  				=      DeeBertModel(lowerCAmelCase__		)
							lowerCamelCase_  				=      nn.Dropout(config.hidden_dropout_prob		)
							lowerCamelCase_  				=      nn.Linear(config.hidden_size   ,			self.config.num_labels		)

							self.init_weights()






		@add_start_docstrings_to_model_forward(lowerCAmelCase__		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=-1   ,			SCREAMING_SNAKE_CASE_=False   ,			)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      self.num_layers
							try:
												lowerCamelCase_  				=      self.bert(
												    lowerCAmelCase__   ,			attention_mask=lowerCAmelCase__   ,			token_type_ids=lowerCAmelCase__   ,			position_ids=lowerCAmelCase__   ,			head_mask=lowerCAmelCase__   ,			inputs_embeds=lowerCAmelCase__   ,			)
												# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

												lowerCamelCase_  				=      outputs[1]

												lowerCamelCase_  				=      self.dropout(lowerCAmelCase__		)
												lowerCamelCase_  				=      self.classifier(lowerCAmelCase__		)
												lowerCamelCase_  				=      (logits,) + outputs[2:]  # add hidden states and attention if they are here
							except HighwayException as e:
												lowerCamelCase_  				=      e.message
												lowerCamelCase_  				=      e.exit_layer
												lowerCamelCase_  				=      outputs[0]

							if not self.training:
												lowerCamelCase_  				=      entropy(lowerCAmelCase__		)
												lowerCamelCase_  				=      []
												lowerCamelCase_  				=      []
							if labels is not None:
												if self.num_labels == 1:
																	#  We are doing regression
																	lowerCamelCase_  				=      MSELoss()
																	lowerCamelCase_  				=      loss_fct(logits.view(-1		)   ,			labels.view(-1		)		)
												else:
																	lowerCamelCase_  				=      CrossEntropyLoss()
																	lowerCamelCase_  				=      loss_fct(logits.view(-1   ,			self.num_labels		)   ,			labels.view(-1		)		)

												# work with highway exits
												lowerCamelCase_  				=      []
												for highway_exit in outputs[-1]:
																	lowerCamelCase_  				=      highway_exit[0]
																	if not self.training:
																						highway_logits_all.append(lowerCAmelCase__		)
																						highway_entropy.append(highway_exit[2]		)
																	if self.num_labels == 1:
																						#  We are doing regression
																						lowerCamelCase_  				=      MSELoss()
																						lowerCamelCase_  				=      loss_fct(highway_logits.view(-1		)   ,			labels.view(-1		)		)
																	else:
																						lowerCamelCase_  				=      CrossEntropyLoss()
																						lowerCamelCase_  				=      loss_fct(highway_logits.view(-1   ,			self.num_labels		)   ,			labels.view(-1		)		)
																	highway_losses.append(lowerCAmelCase__		)

												if train_highway:
																	lowerCamelCase_  				=      (sum(highway_losses[:-1]		),) + outputs
																	# exclude the final highway, of course
												else:
																	lowerCamelCase_  				=      (loss,) + outputs
							if not self.training:
												lowerCamelCase_  				=      outputs + ((original_entropy, highway_entropy), exit_layer)
												if output_layer >= 0:
																	lowerCamelCase_  				=      (
																	    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																	)  # use the highway of the last layer

							return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a    :	int         =    logging.get_logger(__name__)

__a    :	Optional[int]         =    {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Any     					=		'''pegasus'''
			__a    :					Optional[Any]     					=		['''past_key_values''']
			__a    :					Tuple     					=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





			def __init__(  self ,  lowerCAmelCase__=5_02_65 ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=40_96 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=40_96 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=False ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=1 ,  lowerCAmelCase__=1 ,  **lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							__lowercase					     =				vocab_size
							__lowercase					     =				max_position_embeddings
							__lowercase					     =				d_model
							__lowercase					     =				encoder_ffn_dim
							__lowercase					     =				encoder_layers
							__lowercase					     =				encoder_attention_heads
							__lowercase					     =				decoder_ffn_dim
							__lowercase					     =				decoder_layers
							__lowercase					     =				decoder_attention_heads
							__lowercase					     =				dropout
							__lowercase					     =				attention_dropout
							__lowercase					     =				activation_dropout
							__lowercase					     =				activation_function
							__lowercase					     =				init_std
							__lowercase					     =				encoder_layerdrop
							__lowercase					     =				decoder_layerdrop
							__lowercase					     =				use_cache
							__lowercase					     =				encoder_layers
							__lowercase					     =				scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=lowerCAmelCase__ ,  eos_token_id=lowerCAmelCase__ ,  is_encoder_decoder=lowerCAmelCase__ ,  decoder_start_token_id=lowerCAmelCase__ ,  forced_eos_token_id=lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return self.encoder_attention_heads





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return self.d_model